1. Unbalanced Random Matching Markets: the Start Effect of Competition Itai Ashlagi, Yash Kanoria, Jacob Leshno

2. Matching Markets  We study two-sided matching markets where  Agents have private preferences  There are no transfers [Gale & Shapley 1962]  Stable matchings are equilibrium outcomes in matching markets.  Centralized matching markets: Medical residency match (NRMP), School choice (NYC, Boston, New Orleans,…)  Decentralized matching markets: Online dating, labor markets, college admissions  This work: characterizing stable matchings  What can we say about typical outcomes?

3. Background: Stable Matchings (Core Allocations)

4. Example ABC 124 241 332 413 1234 ACAB BACC CBBA  The preferences of 3 men and 4 women:

5. Example ABC 124 241 332 413 1234 ACAB BACC CBBA  A non stable matching ( A and 2 would block)

6. Example ABC 124 241 332 413 1234 ACAB BACC CBBA  The MOSM:  (3 is unmatched)

7. Example ABC 124 241 332 413 1234 ACAB BACC CBBA  The WOSM:  (3 is unmatched)

8. 8 Clearinghouses that find stable matchings Medical Residencies in the U.S. (NRMP) (1952) Abdominal Transplant Surgery (2005) Child & Adolescent Psychiatry (1995) Colon & Rectal Surgery (1984) Combined Musculoskeletal Matching Program (CMMP)  Hand Surgery (1990) Medical Specialties Matching Program (MSMP)  Cardiovascular Disease (1986)  Gastroenterology (1986-1999; rejoined in 2006)  Hematology (2006)  Hematology/Oncology (2006)  Infectious Disease (1986-1990; rejoined in 1994)  Oncology (2006)  Pulmonary and Critical Medicine (1986)  Rheumatology (2005) Minimally Invasive and Gastrointestinal Surgery (2003) Obstetrics/Gynecology  Reproductive Endocrinology (1991)  Gynecologic Oncology (1993)  Maternal-Fetal Medicine (1994)  Female Pelvic Medicine & Reconstructive Surgery (2001) Ophthalmic Plastic & Reconstructive Surgery (1991) Pediatric Cardiology (1999) Pediatric Critical Care Medicine (2000) Pediatric Emergency Medicine (1994) Pediatric Hematology/Oncology (2001) Pediatric Rheumatology (2004) Pediatric Surgery (1992) Primary Care Sports Medicine (1994) Radiology  Interventional Radiology (2002)  Neuroradiology (2001)  Pediatric Radiology (2003) Surgical Critical Care (2004) Thoracic Surgery (1988) Vascular Surgery (1988) Postdoctoral Dental Residencies in the United States  Oral and Maxillofacial Surgery (1985)  General Practice Residency (1986)  Advanced Education in General Dentistry (1986)  Pediatric Dentistry (1989)  Orthodontics (1996) Psychology Internships (1999) Neuropsychology Residencies in the U.S. & CA (2001) Osteopathic Internships in the U.S. (before 1995) Pharmacy Practice Residencies in the U.S. (1994) Articling Positions with Law Firms in Alberta, CA(1993) Medical Residencies in CA (CaRMS) (before 1970) British (medical) house officer positions  Edinburgh (1969)  Cardiff (197x) New York City High Schools (2003) Boston Public Schools (2006), Denver, New Orleans

9. This Paper  So far theory does not explain the success of centralized markets  Theorem [Roth 1982]: No stable mechanism is strategyproof  Theorem [Demange, Gale, Sotomayor (1987)]: Agents can successfully manipulate if only if they have multiple stable partners  This paper provides an explanation for why stable matchings are essentially unique.

10. Random Matching Markets

11. Questions

12. Large core in balanced random markets How large is the core when there n men and n+1 women? What is the average rank of women in the WOSM?

13. Related literature  All documented evidence shows the core is small:  Roth, Peranson (1999) – small core in the NRMP  Hitsch, Hortascu, Ariely (2010) – small core in online dating  Banerjee et al. (2009) – small core in Indian marriage markets  We can explain small cores in restricted settings:  Immorlica, Mahdian (2005) and Kojima, Pathak (2009) show that if one side has short random preference lists the core is small But many agents are unmatched  Small core when preferences are highly correlated (e.g., Holzman, Samet (2013))  Comparative statics on adding men but only in a given extreme stable matching, Crawford (1991)

14. Results – unbalanced markets When there are unequal number of men and women:  The core is small; small difference between the MOSM and the WOSM  Small core despite long lists and uncorrelated preferences  The short side is much better off under all stable matchings; roughly, the short side chooses and the long side gets chosen  sharp effect of competition Out results suggest that real matching markets will have an essentially unique stable matching

20. Theorem 2: One woman makes a difference

21. Theorem 2 (for any k)

22. Theorem 2  That is, with high probability under all stable matchings when the men are on the short side  Men do almost as well as they would if they chose, ignoring women’s preferences.  Women are either unmatched or roughly getting a randomly assigned man.

23. Large imbalance

24. Intuition

25. Strategic implications

26. Proof overview 1 st step: Count proposals under the Men-proposing Deferred Acceptance (which finds the MOSM)

27. Proof overview Calculate the WOSM using:  Algorithm 1: Men-proposing Deferred Acceptance gives MOSM  Algorithm 2: MOSM → WOSM Both algorithms use a sequence of proposals by men Stochastic analysis by sequential revelation of preferences.

28. Men-proposing Deferred Acceptance (Gale & Shapley)  Everyone starts unmatched.  Repeat until all men are matched:  Each rejected or unmatched man proposes to his most preferred woman he has yet proposed to.  Each woman who receives at least one proposal, rejects all but her most preferred proposal so far.

29. Algorithm 2: MOSM → WOSM

30. Illustration of Algorithm 2: MOSM → WOSM

34. New stable match found. Update match and continue.

35. Illustration of Algorithm 2: MOSM → WOSM

41. women who found their best stable partner

42. Illustration of Algorithm 2: MOSM → WOSM

47. Proof idea:  Analysis of men-proposing DA is similar to that of Pittel (1989)  Coupon collectors problem  Analysis of Algorithm 2: MOSM → WOSM more involved.  Track S - set of woman matched already to best stable partner  Once S is large improvement phases are rare  Together, in a typical market, very few agents participate in improvement cycles

48. Why does the S (set of women matched to best stable partners) grows quickly?

49. Further questions

51. Correlated Preferences – absolute correlation

52. Correlated Preferences – pos cross correlation

53. Short Lists

54. Percent of men with multiple stable matches Diff:-100+1+5+10 1002.115.175.315.44.52.3 2002.214.683.614.64.12.1 5002.012.691.013.13.62.0 10001.912.394.512.23.42.0 20001.811.196.711.12.91.7 50001.510.198.410.22.81.5

55. Large Simulations Men's rank under% Men withMen's rank under% Men with MOSMWOSMMultiple StableMOSMWOSM Multiple Stable 101.98 (0.45)2.29 (0.60)13.84 (18.82)1.31 (0.20)1.33 (0.21)1.19 (5.13) 1004.09 (0.72)4.89 (1.08)15.16 (12.98)2.55 (0.26)2.61 (0.27)2.30 (3.15) 1,0006.47 (0.79)7.44 (1.28)11.9 (10.17)4.59 (0.30)4.69 (0.31)1.95 (2.03) 10,0008.80 (0.79)9.80 (1.30)9.45 (8.30)6.88 (0.30)6.98 (0.32)1.46 (1.47) 100,00011.11 (0.83)12.09 (1.31)7.66 (6.60)9.16 (0.31)9.26 (0.32)1.08 (1.02) 1,000,00013.40 (0.80)14.41 (1.27)6.62 (6.04)11.46 (0.30)11.56 (0.32)0.85 (0.80)

56. Conclusion Random unbalanced matching markets are highly competitive:  Essentially a unique stable matching  The short side is highly favored and “gets to choose”  Results suggest that every matching market has a small core  Clearinghouses that implement stable outcomes make it safe for all agents to reveal their information! Remark: in order to find an “optimal” allocation, one can focus on the set of feasible matchings (or on choice menus)